Banks and other financial institutions offer different types of credit card and debit payment card accounts that are designed to meet the needs of both personal consumers and small businesses. While these card accounts have many similarities, they are also differences targeted to the different type of cardholders. For example, in comparison to a personal credit card account, a small business credit card account may feature increased card spending controls, more flexible billing arrangements, improved tracking and reporting functions, easier ability to integrate transaction data into accounting systems, and additional business and travel insurance.
Notwithstanding the advantages of using a dedicated small business account, consumer accounts are frequently used by small businesses to purchase goods and services. In such cases, the small businesses will not receive the benefits of a dedicated small business account, and the bank or financial institution will miss the opportunity to provide such additional benefits and to receive the additional revenue from providing such benefits. It is therefore advantageous for banks and financial institutions to identify consumer accounts that are used to make purchases for small business use and to target these accounts with offers of a small business account.
Traditionally, banks and financial institutions make their targeting or other marketing decisions based upon customer profiles either purchased from a third party provider or generated by analyzing corporate data, which is collected in a data warehouse. In the latter case, the customer profiles are usually developed in response to ad hoc inquires by applying data mining techniques to historical or accumulated transaction data. The accumulated transaction data may include data on merchant-customer transactions over a period of months or years. However, making ad hoc inquiries of accumulated transaction data requires significant time and resources, generates intermittent heavy workloads for key personnel, and places intermittent heavy demands on enterprise resources. Further, customer profiles obtained by conventional data mining may be outdated as they are based on information that has accumulated over a period of many months or years. Accordingly, profiles obtained by conventional data mining are not well suited for rapidly detecting patterns in the purchasing behavior of consumer or small business cardholders.
Recently, MasterCard International Incorporated (“MasterCard”) has developed systems and methods for real-time transaction-based cardholder profiling. See, for example, MasterCard's co-pending U.S. patent application Ser. No. 10/800,875 by Chris Merz, filed Mar. 15, 2004 (“Merz”), which is hereby incorporated by reference in its entirety herein. The real-time transaction-based profiling systems and methods are designed to make a rolling profile summary of each cardholder's behavior available for immediate analysis. A rolling profile summary may contain timely information such as “three purchases were made within one month from vendors within the ‘jewelry and giftware’ category”, “the average purchase amount for this cardholder is $52”, “this cardholder is interested in sports,” etc. The rolling profiles can also contain up-to-date estimates of home ZIP code, age, gender, income, and other demographic or behavioral information.
Consideration is now being given to ways of improving the targeting of small business account card offers to consumer accounts that are being inappropriately used to make purchases for small business use. Attention is directed to ways of predicting small business behavior from consumer card transactions. In particular, attention is directed to real-time transaction-based profiling procedures for more accurately identifying those consumer credit/debit card accounts that are being inappropriately used to make small business purchases.